The present invention relates generally to the field of fault diagnostics, and, more particularly, to a fault diagnostics system which employs a neural network processor, and a related method.
Large systems which are comprised of a plurality of different components or subsystems are susceptible to failures due to a fault in any one (or more) of the components or subsystems thereof. Each type of failure is referred to as a "failure mode". Fault diagnostics systems are utilized in such systems to detect any failures of the system and to diagnose the source of any such detected failures or faults. Fault diagnostics systems ideally should be capable of quickly and accurately diagnosing the source of a fault. Inability to quickly identify the source of a fault can lead to unnecessary component replacement and/or excessive down time of the faulty system. Inaccurate identification of the source of a fault can lead to unnecessary replacement of good components.
An exemplary system in which the fault diagnostics system is of paramount importance is an aircraft. Support and maintenance costs can be a dominant factor in the high life cycle costs of an aircraft. The various subsystems of an aircraft, such as the flight control subsystem, are quite complex. Identifying the source of a fault in such complex subsystems requires a quite sophisticated fault diagnostics system.
The best time to diagnose a fault is during flight, when complete data relating to the fault is available. Post-flight troubleshooting can be time-consuming and often results in unnecessary replacement of good components, which are usually quite expensive. Inability to quickly identify the source of the fault can result in excessive aircraft down time and even loss of a scheduled mission.
Current fault diagnostics systems for commercial and military aircraft include built-in-test software for each of the major subsystems of the aircraft. Such built-in-test software utilizes a separate model for each subsystem or more than one operating component for comparison voting. Either approach entails high development costs. Moreover, current fault diagnostics systems for aircraft have average error rates of 40%-50%, due to the small tolerances between normal and failed conditions of the components.
Based on the above, there presently exists a need in the art for a fault diagnostics system which overcomes the above-described drawbacks and shortcomings of the currently available fault diagnostics systems. The present invention fulfills this need in the art.